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"Ketimpangan pendapatan dan kemiskinan telah menjadi permasalahan utama dalam studi pembangunan sejak tahun 1970-an. Meskipun ada berbagai faktor yang secara teoritis terkait dengan insiden kemiskinan dan ketimpangan pendapatan, pilihan mengenai jenis dan struktur pengeluaran pemerintah sering dikutip sebagai salah satu faktor penentu penting. Namun, bukti ilmiah atas permasalahan ini masih belum bisa disimpulkan, dan penelitian atas kasus di Indonesia masih sangat sedikit. Penelitian ini mencoba untuk berkontribusi dengan memanfaatkan data panel 33 propinsi dari tahun 2005 sampai dengan 2017 untuk menguji pengaruh berbagai jenis pengeluaran pemerintah terhadap ketimpangan pendapatan dan kemiskinan di Indonesia. Dengan menggunakan fixed effect, random effect, dan Seemingly Unrelated Regression (SURE) sistem, penelitian ini menemukan bahwa bantuan sosial, subsidi dan pengeluaran hibah memiliki efek yang tidak signifikan dalam mengurangi ketimpangan pendapatan dan kemiskinan di Indonesia. Namun, bukti empiris menunjukkan bahwa pengeluaran infrastruktur memiliki korelasi negatif dengan ketimpangan pendapatan di daerah perkotaan (ketika menggunakan random effect model), dan daerah pedesaan (ketika menggunakan fixed effect model), keduanya signifikan secara statistik pada tingkat 5%. Selain itu, pengeluaran infrastruktur juga berkorelasi negatif dan signifikan dengan kemiskinan di Indonesia, dan dampaknya lebih signifikan di daerah pedesaan daripada perkotaan.

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The issues of income inequality and poverty have become key issues in development studies since the 1970s. Although there are various factors theoretically associated with the incidence of poverty and income inequality, choices regarding the types and structure of government expenditure are often quoted as one of the crucial determinants. However, the evidence is still inconclusive, and the research about these issues in the case of Indonesia is still minimum. This paper tries to contribute to the discussion by analysing a panel data set of 33 provinces from 2005 to 2017 to examine the effect of different types of government expenditure on income inequality and poverty in Indonesia. Using the fixed effect, random effect, and Seemingly Unrelated Regression (SURE) system, this paper finds that social aid, subsidy and grant expenditure have an insignificant effect on reducing income inequality and poverty in Indonesia. However, the empirical evidence suggests that infrastructure spending has a negative correlation with income inequality in urban areas (when using the random effect model), and rural areas (when using the fixed effect model), both are statistically significant at the 5% level. In addition, infrastructure expenditure is also negatively and significantly correlated with poverty in Indonesia, and the impact is more significant in rural than urban areas."

"Kemiskinan merupakan suatu kondisi yang ditandai dengan ketidakmampuan seseorang dalam memenuhi kebutuhan pokoknya. Berdasarkan publikasi Badan Pusat Statistik (BPS) dari tahun 2017 hingga 2020, Provinsi Papua dan Papua Barat yang berada di Pulau Papua merupakan dua provinsi dengan kemiskinan tertinggi di Indonesia. Oleh karena itu, penelitian ini bertujuan untuk menganalisis variabel-variabel yang signifikan memengaruhi kemiskinan di Pulau Papua. Permasalahan kemiskinan merupakan permasalahan kompleks yang tidak hanya terjadi pada satu waktu dan dapat dipengaruhi oleh aspek kewilayahan atau spasial. Hal ini mengindikasikan perlunya dilakukan suatu penelitian yang melibatkan efek spasial dalam beberapa periode waktu. Sehubungan dengan hal tersebut, maka data spasial dengan struktur panel digunakan dalam penelitian ini. Data penelitian yang merupakan data spasial dengan struktur panel mengakibatkan kemungkinan munculnya pengaruh spasial seperti heterogenitas spasial. Uji heterogenitas spasial dilakukan menggunakan uji Breusch-Pagan. Berdasarkan pengujian didapatkan bahwa terdapat heterogenitas spasial pada data kemiskinan di Pulau Papua tahun 2017-2020 sehingga dilakukan analisis lebih lanjut dengan menggunakan model Geographically Weighted Panel Regression (GWPR). Model GWPR dibentuk menggunakan fungsi pembobot kernel Fixed Gaussian, Fixed Bisquare, dan Fixed Tricube. Hasil penelitian menunjukkan bahwa model GWPR dengan fungsi pembobot kernel Fixed Gaussian merupakan model terbaik dalam memodelkan data kemiskinan di Pulau Papua tahun 2017-2020 dengan koefisien determinasi sebesar 84.93% dan RMSE sebesar 0.013459686. Variabel harapan lama sekolah, angka harapan hidup, rasio gini, pengeluaran per kapita disesuaikan, dan tingkat partisipasi angkatan kerja berpengaruh signifikan terhadap kemiskinan pada minimal satu lokasi di kabupaten/kota di Pulau Papua. Variabel angka harapan hidup dan pengeluaran per kapita disesuaikan merupakan variabel yang paling banyak berpengaruh signifikan terhadap kemiskinan di kabupaten/kota di Pulau Papua.

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Poverty is a condition characterized by a person’s inability to fulfill their basic needs. Based on the publications of Badan Pusat Statistik (BPS) from 2017 to 2020, the provinces of Papua and West Papua on the island of Papua are the two provinces with the highest rates of poverty in Indonesia. Therefore, this study aims to analyze the variables that significantly affect poverty on Papua Island. The problem of poverty is a complex problem that does not only occur at one time and can be influenced by regional or spatial aspects. This indicates the need for a study involving spatial effects over several periods. In this regard, spatial data with panel structure is used in this study. Research data which is spatial data with a panel structure results in the possibility of the emergence of spatial influences such as spatial heterogeneity. A spatial heterogeneity test was performed using the Breusch-Pagan test. Based on the test, it was found that there is spatial heterogeneity in poverty data in Papua Island in 2017-2020 so further analysis will be carried out using the Geographically Weighted Panel Regression (GWPR) model. The GWPR model is formed using the Fixed Gaussian, Fixed Bisquare, and Fixed Tricube kernel weighting functions. The results show that the GWPR model with the Fixed Gaussian kernel weighting function is the best in modeling poverty data in Papua Island in 2017- 2020 with the coefficient of determination of 84.93% and RMSE of 0.013459686. The variables of expected years of schooling, life expectancy, gini ratio, consumption per capita, and labor force participation rate have a significant effect on poverty in at least one location in a district/city on Papua Island. The variables of life expectancy and consumption per capita have a significant effect on poverty in most districts/cities on Papua Island."

"[ABSTRAKPenelitian ini menyelidiki pengaruh perubahan kebijakan fiskal selamapelaksanaan desentralisasi fiskal terhadap ketimpangan di Indonesia. Denganmenggunakan data panel dari 27 provinsi selama periode 2001-2012, penelitianini menjelaskan dampak dari peraturan baru tentang desentralisasi fiskal terhadapketimpangan pengeluaran. Penelitian ini menggunakan koefisien Gini, Theil L,dan Theil T sebagai ukuran ketimpangan, sedangkan variabel independen meliputiindeks desentralisasi fiskal (FDA dan FDB), PDRB per kapita (pcGRDP) danPDRB per kapita kuadrat (pcGRDP kuadrat). Variabel independen PDRB perkapita dan PDRB per kapita kuadrat berfungsi sebagai variabel kontrol.Hasil regresi panel menunjukkan bahwa selama pelaksanaan Undang-Undang Nomor 25 tahun 1999 (2001-2004), desentralisasi fiskal berdampaknegatif pada ketimpangan. Penelitian ini menemukan bahwa desentralisasi fiskalmengakibatkan peningkatan ketimpangan pengeluaran. Semakin besardesentralisasi fiskal menyebabkan semakin besarnya ketimpangan pengeluaran.Perubahan undang-undang desentralisasi fiskal tahun 2004 menunjukkan bahwaketimpangan dapat dikurangi. Meskipun pengaruh desentralisasi fiskal padaketimpangan masih negatif setelah tahun 2004, besar dampaknya jauh lebih kecil.

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ABSTRACTThis study attempts to investigate the effect of change in fiscal regulationduring implementation of fiscal decentralization on inequality in Indonesia. Usinga provincial panel data set consisting of 27 provinces during 2001-2012 periods,this study explains the impact of new regulation on fiscal decentralization onexpenditure inequality. This study employs Gini coefficient, Theil L, and Theil Tas a measure of inequality, while independent variables include indices of fiscaldecentralization (FDA and FDB), per capita GRDP (pcGRDP) and per capitaGRDP squared (pcGRDP squared). Independent variables, pc GRDP and pcGRDPsquared serve as control variables.The panel regression results show that during the implementation of the lawNo.25/1999 (i.e., from 2001 to 2004), fiscal decentralization has a negative impacton inequality. In particular, this study finds that fiscal decentralization appears tohave increased expenditure inequality. Higher degree of fiscal decentralizationviiUniversitas Indonesiainduces larger expenditure inequality. However, the implementation of the revisedfiscal decentralization law in 2004 seems to have mitigated inequality. Though theeffect of fiscal decentralization on inequality is still negative after 2004, themagnitude of the effect is much smaller.;This study attempts to investigate the effect of change in fiscal regulationduring implementation of fiscal decentralization on inequality in Indonesia. Usinga provincial panel data set consisting of 27 provinces during 2001-2012 periods,this study explains the impact of new regulation on fiscal decentralization onexpenditure inequality. This study employs Gini coefficient, Theil L, and Theil Tas a measure of inequality, while independent variables include indices of fiscaldecentralization (FDA and FDB), per capita GRDP (pcGRDP) and per capitaGRDP squared (pcGRDP squared). Independent variables, pc GRDP and pcGRDPsquared serve as control variables.The panel regression results show that during the implementation of the lawNo.25/1999 (i.e., from 2001 to 2004), fiscal decentralization has a negative impacton inequality. In particular, this study finds that fiscal decentralization appears tohave increased expenditure inequality. Higher degree of fiscal decentralizationviiUniversitas Indonesiainduces larger expenditure inequality. However, the implementation of the revisedfiscal decentralization law in 2004 seems to have mitigated inequality. Though theeffect of fiscal decentralization on inequality is still negative after 2004, themagnitude of the effect is much smaller.;This study attempts to investigate the effect of change in fiscal regulationduring implementation of fiscal decentralization on inequality in Indonesia. Usinga provincial panel data set consisting of 27 provinces during 2001-2012 periods,this study explains the impact of new regulation on fiscal decentralization onexpenditure inequality. This study employs Gini coefficient, Theil L, and Theil Tas a measure of inequality, while independent variables include indices of fiscaldecentralization (FDA and FDB), per capita GRDP (pcGRDP) and per capitaGRDP squared (pcGRDP squared). Independent variables, pc GRDP and pcGRDPsquared serve as control variables.The panel regression results show that during the implementation of the lawNo.25/1999 (i.e., from 2001 to 2004), fiscal decentralization has a negative impacton inequality. In particular, this study finds that fiscal decentralization appears tohave increased expenditure inequality. Higher degree of fiscal decentralizationviiUniversitas Indonesiainduces larger expenditure inequality. However, the implementation of the revisedfiscal decentralization law in 2004 seems to have mitigated inequality. Though theeffect of fiscal decentralization on inequality is still negative after 2004, themagnitude of the effect is much smaller., This study attempts to investigate the effect of change in fiscal regulationduring implementation of fiscal decentralization on inequality in Indonesia. Usinga provincial panel data set consisting of 27 provinces during 2001-2012 periods,this study explains the impact of new regulation on fiscal decentralization onexpenditure inequality. This study employs Gini coefficient, Theil L, and Theil Tas a measure of inequality, while independent variables include indices of fiscaldecentralization (FDA and FDB), per capita GRDP (pcGRDP) and per capitaGRDP squared (pcGRDP squared). Independent variables, pc GRDP and pcGRDPsquared serve as control variables.The panel regression results show that during the implementation of the lawNo.25/1999 (i.e., from 2001 to 2004), fiscal decentralization has a negative impacton inequality. In particular, this study finds that fiscal decentralization appears tohave increased expenditure inequality. Higher degree of fiscal decentralizationviiUniversitas Indonesiainduces larger expenditure inequality. However, the implementation of the revisedfiscal decentralization law in 2004 seems to have mitigated inequality. Though theeffect of fiscal decentralization on inequality is still negative after 2004, themagnitude of the effect is much smaller.]"

"Kemiskinan merupakan masalah kependudukan yang sangat kompleks bukan saja dilihat dari perspektif determinan yang sangal bervariasi antar individu tetapi lebih dari itu kemiskinan merupakan fenomena yang dinamis. Pada tahun 2005-2007, BPS menghitung bahwa sebagian besar penduduk miskin merupakan miskin transient atau miskin sementara (60,4%) dan 39,6 persen berstatus miskin kronis atau selalu berstatus miskin. Banyaknya miskin transient identik dengan tingginya dinarnika masuk dan keluar kategorl miskin, ditambah dengan penumpukan penduduk pada level subsisten menunjukkan tingginya tingkat kerentanan individu untuk menjadi miskin. Penelitian ini untuk mengetahui bagaimana peran karakteristik rumahtangga serta perubahan karakteristik tersebut dalam memicu perubahan status miskin rumahtangga baik masuk menjadi miskin (entry to poor) maupun keluar dari miskin (exit from poor). Event perubahan karakteristik mencakup perubahan demografis, labor marketf dan human capital serta perubahan kondisi makro. Model menggunakan Multilevel Logistik Biner untuk melibat peran variabel­ variabel tersebut dalam menentukan probabilita Entry to Poor dan Exit from Poor. Hasil estimasi menunjukkan pada rumahtangga tidak miskin. rentanitas menjadi miskin terutama dipengaruhi oleh latar belakang sosial ekonomi responden, baru kemudian faktor peristiwa demografis dan ketenagakerjaan. Sebaliknya pada rumahtangga miskin, peristiwa demografis menyumbang peran terbesar pada probabilita keluar dari miskin dibanding latar belakang sosial ekonomi dan peristiwa ketenagakerjaan. Pertumbuhan ekonomi terbukti tidak berasosiasi?

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The poverty is complex population probiem in order to has vary determinant among individu. More than that perspective, poverty is dynamics phenomena. Along 2005-2007, BPS-Statistics of Indonesia count that majority of poor popu!ation was transient poor (60.4%) and 39.6 percent was chronnic poor or persistens poor. This figure desribes high dynamics entry and exit from poor category, In other side, the Indonesia population has phenomena that high density at around of poverty line (subsistent level) area. It shows high vulnerability of individu to fall in poor. This study to found how role of household characteristics also their changes be trigger mobility of household povert state as well as entry to poor and exit from poor. The events of characteristics changes covers demographic changes, labor market, human capital also characteristic makro changes. The study uses Multilevel Binary Logistic Model to find role of these variabels in determine probability of entry to poor and exit from poor. Result of estimation suggests that no poor household, vulnerability being poor mainly determined by social economics factor of household then respectively by demographic and labor market events. Oppositely, on non poor household demographic events are largest role to contribute probability exit from poor than social economic background and labor market events. Other fact, economic growth has no association with probability of entty to poor and exit from poor. However employment rate significantly associated. Other finding, into aging time elder population has increasing in probability entry to poor that trigger by split of worker household members. Adding of household members has effect increasing?"

"Infrastruktur mempunyai peranan penting dalam pertumbuhan ekonomi. Infrastruktur yang dianggap paling penting dalam pertumbuhan ekonomi di Kalimantan adalah infrastruktur jalan dan infrastruktur listrik. Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh ketersediaan infrastruktur jalan dan listrik terhadap pertumbuhan ekonomi di Kalimantan. Berdasarkan hasil regresi ekonometrika data panel dapat diketahui elastisitas infrastruktur jalan maupun infrastruktur listrik terhadap pertumbuhan ekonomi di Kalimantan. Dengan dilakukan perhitungan sumber pertumbuhan dapat diketahui pula kontribusi masing-masing infrastruktur dan juga total faktor produktivitas terhadap pertumbuhan ekonomi di Kalimantan dalam periode 1994-2008.

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Infrastructure has important role for economic growth. Infrastructure considered being the most important in Kalimantan are road and electricity. This research attempts to know the influence on availability of road and electricity to economic growth in Kalimantan. Based on result of panel data econometrical regression, elasticity of either road or electricity can be calculated. By growth accounting, contribution of each infrastructure and also total factor productivity for economic growth in Kalimantan for period of 1994-2008 can also be calculated."

"Tesis ini membahas bagaimana pengaruh investasi (PMDN dan PMA) serta tenaga kerjaterhadap pertumbuhan ekonomi provinsi-provinsi di Pulau Jawa (DKI Jakarta, JawaBarat, Jawa Tengah, Jawa Timur dan DI Yogyakarta). Penelitian ini menggunakanpanel data tahun 1990-2007. Penelitian ini dimulai dengan analisis deskriptif, analisisregresi (cross section OLS), panel data (common dan fixed effect) serta uji chow untukmenentukan model terbaik dan didapatkan fixed effect sebagai model terbaik. Penelitianini menunjukkan bahwa variabel PMDN, tenaga kerja dan periode waktu (dummykrisis) berpengaruh terhadap PDRB sedangkan PMA tidak berpengaruh terhadapPDRB.

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This thesis discuses how the impact of investment (PMDN and PMA) and labor to economic growth in the provinces in Java Island (Jakarta, West Java, Central Java, East Java and Yogyakarta). This study uses panel data year 1990-2007. This research began with descriptive analysis, regression analysis (OLS Cross Section), panel data (fixed and common effect) and the chow test to determine the best model and established as a fixed effect model best. Research shows that this variable PMDN, labor and time period (crisis dummy) effect on GDP regional while PMA did not effect the GDP regional."